The invention relates to a method for preventive maintenance diagnosis of equipment which utilizes a six sigma quality approach to forecast incipient failure. Equipment is expected to operate without interruption over a predetermined period of time. For example, locomotives are expected to operate without interruption for a period of typically 92 days. At the end of the 92 day period, scheduled routine shop maintenance occurs, where the opportunity exists to replace worn or damaged components which might prevent the locomotive from completing the next 92 day mission. It would be beneficial to identify equipment problems before such problems result in failures with sufficient lead time to correct deficiencies during scheduled maintenance.
Accordingly, there is a need in the art for an improved method of diagnosing equipment.
An exemplary embodiment of the invention is directed to a method of predicting failures in equipment including at least one sensor for generating sensor data corresponding to a sensed parameter. The method includes monitoring the sensor data during normal operation of the equipment. The sensor data during normal operation is compared to a model prediction of the sensor data to determine variance between the sensor data and the model prediction. The model is calibrated to minimize the variance between the model prediction and the sensor data. Error in the sensor data is determined and an error condition is generated upon detection of an error in the sensor data.